Understanding the tree growth process is essential for sustainable forest management. Future yields are affected by various forest management regimes such as thinning; therefore, accurate predictions of tree growth are needed under various thinning intensities. This study compared the accuracy of individual-level distance-independent diameter growth models constructed for different thinning intensities (thinning intensity-dependent multiple models: TDM model) against the model designed to include all thinning intensities (thinning intensity-independent single model: TIS model) to understand how model accuracy is affected by thinning intensity. We used long-term permanent plot data of Japanese cedar (Cryptomeria japonica
) stands in Japan, which was gathered from four plots where thinning was conducted at different thinning intensities: (1) intensive (41% and 38% of trees removed at 25 and 37 years old, respectively), (2) moderate (38% and 34%), (3) light (32% and 34%), and (4) no thinning. First, we specified high interpretability distance-independent competition indices, and we compared the model accuracy both in TDM and TIS models. The results show that the relative spacing index was the best competition index both in TDM and TIS models across all thinning intensities, and the differences in the RMSE (Root mean square error) and rRMSE (relative RMSE) in both TDM and TIS models were 0.001–0.01 cm and 0.2–2%, respectively. In the TIS model, rRMSE varied with thinning intensity; the rRMSE was the lowest for moderate thinning intensity (45.8%) and the highest for no thinning (59.4%). In addition, bias values were negative for the TIS model for all thinning intensities. These results suggest that the TIS model could express diameter growth regardless of thinning intensities. However, the rRMSE had varied with thinning intensity and bias had negative values in the TIS model. Therefore, more model improvements are required for accurate predictions of long-term growth of actual Japanese cedar stands.
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